1. Field of the Invention
The present invention is directed to data display. It particularly concerns effectively displaying high-dimensional and complex relational data.
2. Background Information
It is now commonplace to employ computers to sift desired information from databases far too large for individual-human comprehension. Software has been developed for performing analysis of a highly sophisticated nature, and such software is often able to detect trends and patterns in the data that would, as practical matter, be impossible for an individual human being to find.
The converse is often also true. Particularly when the question to be asked does not lend itself to easy definition, computers often have difficulty detecting patterns that are readily apparent to human beings. And this human capability is best brought to bear when the data in question are presented graphically. Data presented graphically usually are more readily understandable than the same data presented only in, say, tabular form. But the degree of the resultant understanding greatly depends on the nature of the display, and determining what the appropriate display should be can present a significant problem.
True, some data almost automatically suggest the type of presentation to which they are best suited. The speed of an airplane as a function of time, for instance, would in most cases simply be presented in a simple x-y plot. And there rarely is any question about the general form of display appropriate to the data that a camera takes. In the former case, the presentation is trivial, since speed and time are the only variables, so they are readily associated with two presentation axes. In the latter, camera case, the data suggest the mode of presentation just as readily, since the domain is a two-dimensional scene and the range is spanned by the colors conventionally employed in printing or presentation on a display screen.
But the way to represent many other types of data is significantly harder to determine. An example is hyperspectral data. Typically, such data are similar to those that result from a camera in the sense that the domain is usually a two-dimensional scene. But the value taken for each picture element (“pixel”) in the scene is not a vector representing visible-color components, such as red, green, and blue or cyan, magenta, and yellow. Instead, it is a vector consisting of a relatively large number of components, each of which typically represents some aspect of the radiation received from a respective wave-length band. And the bands often fall outside the visual range. Because of the data's high dimensionality and the limited dimensionality of human visual perception, some degree of selectivity in data presentation is unavoidable, and the decisions that are involved in making the selections have a significant impact on the presentation's usefulness to the human viewer.
High dimensionality also occurs in other kinds of data. In large medical, forensic, and intelligence databases, for example, data objects may represent respective individual people, and the dimensions may be age, gender, height, weight, income, etc.
And presentation problems can arise even in data sets that are not necessarily high-dimensional. Consider link analysis, for example. This type of analysis is used to study subjects as disparate as communications networks and criminal enterprises. Its purpose is to find helpful patterns in the connections between studied entities. To help the user detect such patterns, nodes on a display represent various entities, and lines connecting the nodes represent various relationships between them. In the case of communications networks, for example, the nodes may be, say, Internet Protocol (“IP”) routers, and the lines would represent the interconnecting communication links. In the case of a criminal enterprise, the nodes may represent people, organizations, buildings, or other entities under surveillance, while the lines may represent known communications between the entities or represent other relationships, such as ownership, legal control, etc. If the amount of data being presented is large, the resulting diagram can be hard to comprehend even if the underlying data dimensionality is low.
To help human users employ such diagrams effectively, presentation systems have provided features that make important patterns “stand out” from the other data represented. For example, some link-analysis systems employ color, thickness, etc. to highlight the nodes and/or relationships that meet criteria of particular interest. A similar approach is commonly used in “brushing,” which is sometimes used when representations of the same data objects are displayed simultaneously in different relative locations in different displays. (The displays can be on the screens of different monitors, for example, or on different parts of a single monitor's screen.) In brushing, a user employs a mouse or other device to select a subset of the objects represented by icons in one display, and the display system highlights other display's objects that represent the same objects.